title: How well do mean field theories of spiking quadratic-integrate-and-fire networks work in realistic parameter regimes?
abstract: We use mean field techniques to compute the distribution of excitatory and inhibitory firing rates in large networks of randomly connected spiking quadratic integrate and fire neurons. These techniques are based on the assumption that activity is asynchronous and Poisson. For most parameter settings these assumptions are strongly violated; nevertheless, so long as the networks are not too synchronous, we find good agreement between mean field prediction and network simulations. Thus, much of the intuition developed for randomly connected networks in the asynchronous regime applies to mildly synchronous networks.
authors.affiliation: Gatsby Computational Neuroscience Unit, University College London, London, UK, agnieszka@gatsby.ucl.ac.uk.
authors.forename: Agnieszka
authors.initials: A
authors.lastname: Grabska-Barwińska
authors.forename: Peter E
authors.initials: PE
authors.lastname: Latham
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D003198:Computer Simulation; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009433:Neural Inhibition / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D013569:Synapses / Q000502:physiology